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Classification of pilots' mental states using a multimodal deep learning network

Authors
Han, Soo-YeonKwak, No-SangOh, TaegeunLee, Seong-Whan
Issue Date
1월-2020
Publisher
ELSEVIER
Keywords
MDL; EEG; ECG; Respiration; EDA; Pilot' s mental states
Citation
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, v.40, no.1, pp.324 - 336
Indexed
SCIE
SCOPUS
Journal Title
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
Volume
40
Number
1
Start Page
324
End Page
336
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58436
DOI
10.1016/j.bbe.2019.12.002
ISSN
0208-5216
Abstract
An automation system for detecting the pilot's diversified mental states is an extremely important and essential technology, as it could prevent catastrophic accidents caused by the deteriorated cognitive state of pilots. Various types of biosignals have been employed to develop the system, since they accompany neurophysiological changes corresponding to the mental state transitions. In this study, we aimed to investigate the feasibility of a robust detection system of the pilot's mental states (i.e., distraction, workload, fatigue, and normal) based on multimodal biosignals (i.e., electroencephalogram, electrocardiogram, respiration, and electrodermal activity) and a multimodal deep learning (MDL) network. To do this, first, we constructed an experimental environment using a flight simulator in order to induce the different mental states and to collect the biosignals. Second, we designed the MDL architecture - which consists of a convolutional neural network and long short-term memory models - to efficiently combine the information of the different biosignals. Our experimental results successfully show that utilizing multimodal biosignals with the proposed MDL could significantly enhance the detection accuracy of the pilot's mental states. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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